code: def test_model_from_config(self):
      input_shape = (1, 3)
      input_data = np.random.normal(size=input_shape)
      expected, k_weights, k_config = _model_predict_keras(
          input_data, input_shape
      )
  
      with tf.name_scope("TFE"):
          x = tfe.define_private_input(
              "inputter", lambda: tf.convert_to_tensor(input_data)
          )
  
          tfe_model = tfe.keras.models.model_from_config(k_config)
          tfe_model.set_weights(k_weights)
          y = tfe_model(x)
          actual = y.reveal().to_native()
  
          np.testing.assert_allclose(
              actual, expected, rtol=1e-2, atol=1e-3
          )
  
file_path: tf-encrypted/tf_encrypted/keras/models/sequential_test.py
imports: import unittest
  import numpy
  import tensorflow
  import tf_encrypted
  from tf_encrypted.keras import Sequential
  from tf_encrypted.keras.layers import Dense
label_desc: Use image node_id 2 for calling the TestSequential obj's underlying member method code with example usage: obj.test_model_from_config() without return types